Segmentation techniques are widely used in diagnostic imaging applications (or simply imaging applications); generally speaking, any segmentation technique is aimed at segmenting a representation of a body-part of a patient under analysis (for example, a digital image thereof) into two or more segments, each one comprising a portion of the representation of the body-part with substantially homogenous characteristics.
For example, segmentation techniques may be used for the assessment of blood perfusion in Dynamic Contrast-Enhanced Ultrasound (DCE-US) imaging applications. In this case, an ultrasound contrast agent (UCA), or simply contrast agent, acting as an efficient ultrasound reflector, is administered to the patient. The contrast agent flows at the same velocity as red-blood cells in the patient, so that its detection by recording echo signals that are returned in response to ultrasound waves provides information about the corresponding blood perfusion. Particularly, for each location of the body-part wherein the echo signal has been recorded, the value of a perfusion parameter that characterizes it (such as a peak enhancement) is calculated (for example, from a parametric function fitting the echo signal over time). A parametric image is built by assigning, to each pixel representing a location of the body-part, the corresponding perfusion parameter value. The parametric image may be used to calculate a consolidated value of the perfusion parameter values (for example, their mean value) in an analysis region of the body-part comprising a lesion; typically, the consolidated value of the analysis region is expressed in relative terms with respect to the consolidated value of the perfusion parameter values in a control region of the body-part comprising healthy tissue. This relative consolidated value provides a quantitative characterization of the lesion, which may be used, for example, in its therapeutic follow-up.
However, the consolidated values of the analysis region and of the control region are intrinsically affected by a certain degree of inaccuracy, since they are calculated globally in regions of the body-part that are not perfectly homogenous; for example, the analysis region may comprise necrotic tissues that are hypo-perfused and the control region may comprise big blood vessels that are hyper-perfused. These hypo-perfused and hyper-perfused portions of the analysis region and of the control region, respectively, cause errors in their consolidated values that reflect in the relative consolidated value of the analysis region. This drawback is particularly acute in the therapeutic follow-up, wherein even subtle changes in the perfusion of the lesion during its treatment may be indicative of the effectiveness of the treatment.
Alternatively, WO-A-2011/026866 (the entire disclosure of which is herein incorporated by reference) discloses determining an analysis function for each pixel of an analysis area (defining a region of interest) by fitting its echo-power signal by a model function of time (for example, a lognormal distribution function), and determining a reference function for a reference area (including healthy parenchyma) by fitting an average of its echo-power signals by the same model function of time. For each pixel of the analysis area, a difference function is calculated by subtracting the reference function from its analysis function; the pixel is then assigned to different classes according to a polarity trend of its difference function (i.e., positive unipolar class, negative unipolar class, positive-to-negative bipolar class, and negative-to-positive bipolar class). For this purpose, it is possible to calculate a positive energy and a negative energy of the difference function; when the sum of the positive energy and the negative energy is higher than a significance threshold, a relative positive energy and a relative negative energy are calculated. If the relative positive energy or the negative relative energy exceeds a discrimination threshold, the difference function is assigned to the positive unipolar class or to the negative unipolar class, respectively; conversely, the difference function is assigned to the positive-to-negative bipolar class or to the negative-to-positive bipolar class according to an order of its main change of polarity.
Segmentation techniques may also be used in Ultrasound Molecular Imaging (UMI) applications. In this case, a target-specific contrast agent is used; the target-specific contrast agent is adapted to reach a specific biological target (for example, a lesion), and then remain immobilized thereon by means of a specific interaction. The detection of any particles of the target-specific contrast agent that are immobilized in the body-part allows identifying the corresponding lesion that would otherwise be difficult to discover; moreover, the quantification of these immobilized particles allows determining a status of the lesion, which may be used, for example, in its therapeutic follow-up.
However, the detection and the quantification of the immobilized particles of the target-specific contrast agent is hindered by the fact that only a small fraction of the total amount thereof actually reaches the target and remains immobilized thereon; most of the target-specific contrast agent continues instead to circulate for quite a long time (such as, several minutes)—for example, until it is filtered out by the lungs and/or in the liver of the patient. Therefore, the particles of the target-specific contrast agent in the body-part comprise both a contribution of the particles that are immobilized thereon and a contribution of the particles that are still circulating; as a consequence, it is quite difficult to discriminate between the two contributions (especially at early times after the administration of the target-specific contrast agent).